The Trade-Off Between Sanitizer Resistance and Virulence Genes: Genomic Insights into E. coli Adaptation
Bibliographic record
Abstract
Background: Escherichia coli is one of the most studied bacteria worldwide due to its genetic plasticity. Recently, in addition to characterizing its pathogenic potential, research has focused on understanding its resistance profile to inhibitory agents, whether these be antibiotics or sanitizers. Objectives: The present study aimed to investigate six of the main serogroups of foodborne infection (O26, O45, O103, O111, O121, and O157) and to understand the dynamics of heterogeneity in resistance to sanitizers derived from quaternary ammonium compounds (QACs) and peracetic acid (PAA) using whole-genome sequencing (WGS). Methods: Twenty-four E. coli strains with varied resistance profiles to QACs and PAA were analyzed by WGS using NovaSeq6000 (150 bp Paired End reads). Bioinformatic analyses included genome assembly (Shovill), annotation via Prokka, antimicrobial resistance gene identification using Abricate, and core-genome analysis using Roary. A multifactorial multiple correspondence analysis (MCA) was conducted to explore gene–sanitizer relationships. In addition, a large-scale analysis utilizing the NCBI Pathogen Detection database involved a 2 × 2 chi-square test to examine associations between the presence of qac and stx genes. Results: The isolates exhibited varying antimicrobial resistance profiles, with O45 and O157 being the most resistant serogroups. In addition, the qac gene was identified in only one strain (S22), while four other strains carried the stx gene. Through multifactorial multiple correspondence analysis, the results obtained indicated that strains harboring genes encoding Shiga toxin (stx) presented profiles that were more likely to be sensitive to QACs. To further confirm these results, we analyzed 393,216 E. coli genomes from the NCBI Pathogen Detection database. Our results revealed a significant association (p < 0.001) between the presence of qac genes and the absence of stx1, stx2, or both toxin genes. Conclusion: Our findings highlight the complexity of bacterial resistance mechanisms and suggest that non-pathogenic strains may exhibit greater tolerance to QAC sanitizer than those carrying pathogenicity genes, particularly Shiga toxin genes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".